We could also use Gaussian distribution for our model whose effect was analogous to using Euclidean distance.
We empirically show that vMF provides superior results to Gaussian distribution when using pre-trained features.
Our results support related works in supervised few-shot learning [1]}, [2]} where using cosine similarity outperforms euclidean distance measure.
The underlying reason for this is well-studied by Wang  [3]}; Softmax loss used in the pre-training tends to create a `radial' feature distribution where direction specifies the semantic classes while magnitude decides the classification confidence.
